U.S. patent application number 12/771550 was filed with the patent office on 2011-11-03 for apparatus, system and method for predictive modeling to design, evaluate and optimize ophthalmic lenses.
This patent application is currently assigned to AMO REGIONAL HOLDINGS. Invention is credited to Hendrik A. Weeber.
Application Number | 20110270596 12/771550 |
Document ID | / |
Family ID | 44343973 |
Filed Date | 2011-11-03 |
United States Patent
Application |
20110270596 |
Kind Code |
A1 |
Weeber; Hendrik A. |
November 3, 2011 |
Apparatus, System and Method for Predictive Modeling to Design,
Evaluate and Optimize Ophthalmic Lenses
Abstract
An apparatus, system and method for predictive modeling to
design, evaluate and optimize ophthalmic lenses is disclosed.
Ophthalmic lenses may include, for example, contacts, glasses or
intraocular lenses (IOLs). The apparatus, system and method may
include a design tool for designing a lens for implantation in an
eye having a plurality of characteristics, a simulator for
simulating performance of the lens in at least one modeled eye
having the plurality of characteristics, at least one input for
receiving clinical performance of the lens in the eye having the
plurality of characteristics, a comparator for comparing outcomes
of the clinical performance and the simulated performance, and an
optimizer for optimizing a subsequent one of the outcome of the
clinical performance responsive to modification of the lens in
accordance with modification to the simulated performance.
Inventors: |
Weeber; Hendrik A.;
(Groningen, NL) |
Assignee: |
AMO REGIONAL HOLDINGS
Quarryvale
IE
|
Family ID: |
44343973 |
Appl. No.: |
12/771550 |
Filed: |
April 30, 2010 |
Current U.S.
Class: |
703/11 ;
351/159.76 |
Current CPC
Class: |
G02C 7/02 20130101; A61F
2240/002 20130101; A61F 2/16 20130101; G02C 7/024 20130101; A61B
3/0025 20130101; G02C 7/04 20130101; G02C 7/06 20130101; G02C 7/041
20130101 |
Class at
Publication: |
703/11 ;
351/177 |
International
Class: |
G06G 7/60 20060101
G06G007/60; G02C 7/02 20060101 G02C007/02 |
Claims
1. A system for optimizing a clinical implementation of an
ophthalmic lens, wherein the ophthalmic lens is indicated for
treating at least one condition of a set of eyes, comprising: a
plurality of eye models associated with at least one processor,
wherein ones of said plurality of eye models are indicative of the
at least one condition of the set of eyes; a simulator provided by
the at least one processor that models the ophthalmic lens in at
least one of said plurality of eye models having at least first
characteristics, wherein said simulator outputs a first outcome of
the ophthalmic lens in the at least one of said plurality of eye
models; at least one clinical input to the at least one processor
comprising at least second characteristics of a clinical eye
associated with the clinical implementation, wherein the second
characteristics at least substantially overlap the first
characteristics, and a second outcome indicative of the clinical
implementation of the at least one ophthalmic lens; and a
comparator instantiated by the at least one processor that compares
the first outcome and the second outcome, and that compares
differences between the first outcome and the second outcome to a
predetermined tolerance threshold; wherein the processor optimizes
at least the first outcome to bring the differences within the
predetermined tolerance threshold.
2. The system of claim 1, wherein the first and the second outcomes
comprise a visual acuity for at least one predetermined viewing
condition.
3. The system of claim 2, wherein the viewing condition comprises
viewing distance at at least one predefined light level.
4. The system of claim 1, wherein the first and second outcomes
comprise a contrast sensitivity for at least one predetermined
viewing condition.
5. The system of claim 4, wherein the viewing condition comprises
viewing distance at at least one predefined light level.
6. The system of claim 1, wherein the second outcome comprises a
clinical outcome parameter.
7. The system of claim 1, wherein at least one of said at least one
clinical input comprises a remote input.
8. The system of claim 9, wherein the remote input is provided over
at least one network.
9. The system of claim 1, wherein at least one of said at least one
clinical input comprises a local input.
10. The system of claim 1, wherein the condition of the set of eyes
is at least one selected from the group consisting of cataracts,
presbyopia, chromatic and spherical aberration, optical
aberrations, ocular geometry, post-LASIK, myopia, retinal
conditions, neural conditions and combinations thereof.
11. The system of claim 1, wherein the predetermined tolerance
threshold comprises a comparative level of visual acuity.
12. The system of claim 1, wherein the predetermined tolerance
threshold comprises a comparative level of contrast
sensitivity.
13. The system of claim 1, further comprising at least one
graphical user interface, wherein at least said clinical input is
provided from, and the optimization is provided to, said at least
one graphical user interface.
14. The system of claim 1, wherein the ophthalmic lens comprises an
IOL.
15. A method of optimizing a design of an ophthalmic lens,
comprising: receiving a first lens design for the ophthalmic lens;
simulating an outcome provided by the first lens design in at least
one modeled eye having a plurality of first characteristics;
receiving a clinical outcome of the first lens design in a
plurality of patient eyes having a plurality of second
characteristics; comparing the clinical outcome and the simulated
outcome; iteratively optimizing said simulating in accordance with
said comparing; and iteratively optimizing the first lens design to
a second lens design in accordance with said comparing.
16. The method of claim 15, wherein the first lens design has
associated therewith a first design parameter.
17. The method of claim 15, wherein the first characteristics and
the second characteristics are substantially equivalent.
18. The method of claim 15, wherein the first characteristics
include at least one of pupil size, off-axis vision, distance
vision, corneal power, residual accommodation, gender, age and
race, cataracts, presbyopia, chromatic and spherical aberration,
optical aberrations, ocular geometry, post-LASIK, myopia, retinal
conditions, and neural conditions.
19. The method of claim 15, wherein the first characteristics
include at least one of particular aberrations and aberrations of
particular severity.
20. The method of claim 19, wherein the particular aberrations
include at least one of astigmatism, presbyopia, and chromatic and
spherical aberration.
21. The method of claim 15, wherein the second characteristics
include at least one of pupil size, off-axis vision, distance
vision, corneal power, residual accommodation, gender, age, race,
cataracts, presbyopia, chromatic and spherical aberration, optical
aberrations, ocular geometry, post-LASIK, myopia, retinal
conditions, and neural conditions.
22. The method of claim 15, wherein the second characteristics
include at least one of particular aberrations and aberrations of
particular severity.
23. The method of claim 15, wherein said comparing comprises
comparing a difference between the clinical outcome and the
simulated outcome to a predetermined tolerance threshold.
24. The method of claim 15, wherein said iteratively optimizing
comprises at least modifying said simulating.
25. The method of claim 15, wherein said optimizing is responsive
to different weightings of ones of the first characteristics.
26. The method of claim 25, wherein said comparing is based on the
different weightings.
27. The method of claim 15, wherein said optimizing is responsive
to different weightings of the first outcome and the second
outcome.
28. A design apparatus for an IOL, comprising: an IOL design tool
for designing the IOL for implantation in a set of eyes having a
plurality of first characteristics; a simulator for simulating
performance of the IOL in at least one modeled eye having a
plurality of second characteristics; at least one input for
receiving clinical performance of the IOL in the set of eyes having
the plurality of first characteristics; a comparator for comparing
outcomes of the clinical performance and the simulated performance;
and an optimizer for optimizing a subsequent outcome of the
clinical performance responsive to modification of the IOL in
accordance with at least one modification to the simulated
performance.
29. The design apparatus of claim 28, wherein the characteristics
comprise weighted characteristics.
30. The design apparatus of claim 28, said optimizer further
comprising a second optimizer for optimizing said simulator.
31. A method of optimizing a design of an ophthalmic lens,
comprising: receiving a first lens design for the ophthalmic lens;
simulating an outcome provided by the at least one first lens
design in at least one modeled eye having a plurality of first
characteristics; receiving a clinical outcome of the first lens
design in a plurality of patient eyes having a plurality of second
characteristics; comparing the clinical outcome and the simulated
outcome; iteratively optimizing said simulating in accordance with
said comparing; and iteratively optimizing a second lens design to
a third lens design in accordance with said iteratively optimized
simulating.
32. The method according to claim 31, wherein the outcome comprises
at least one of a visual acuity and a contrast sensitivity for at
least one predefined viewing condition.
33. The method according to claim 31, wherein the first lens design
comprises a monofocal lens, and the second lens design comprises a
presbyopia correcting lens.
34. The method according to claim 33, wherein the second lens
design comprises a multifocal lens.
35. The method according to claim 31, wherein the first lens design
comprises a monofocal lens and a presbyopia correcting lens.
36. The method according to claim 35, wherein the presbyopia
correcting lens comprises a multifocal lens.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] N/A
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention is directed to lens design and, more
particularly, to an apparatus, system and method for predictive
modeling to design, evaluate and optimize ophthalmic lenses.
[0004] 2. Description of the Background
[0005] Visual acuity is the clarity of vision, and more
specifically is the spatial resolving power of a visual system.
Visual acuity is thus the spatial level of detail that can be
resolved by the visual system, and may be limited by physiological
factors of the patient, such as optical factors within the eye
and/or neural factors. Typically, visual acuity is heuristically
obtained in an ophthalmic medical practice by presentation of a
letter chart to the patient. Visual acuity may be calculated in
accordance with the spatial frequency at which the eye's modulation
transfer function (MTF) intersects with its neural threshold
function (NTF).
[0006] The optical transfer function (OTF) describes the spatial
variation in a optical system as a function of spatial frequency.
The OTF may account for aberration in the optical system, and has a
magnitude of the MTF and a phase defined by the phase transfer
function (PTF).
[0007] An ophthalmic lens may be used to correct aberrations of the
eye, as defined using the OTF, for example. An ophthalmic lens may
be, for example, an intraocular lens (IOL) that may be surgically
implanted, such as a spheric, aspheric, diffractive, refractive,
accommodating or injectable IOL, an optical inlay or overlay, or
contact lenses or glasses, for example. An IOL is typically
implanted to correct medical conditions, such as cataracts and/or
presbyopia, for example.
[0008] On an individual basis, the modeling of the visual acuity
(VA) of ophthalmic lenses using the intersection of the MTF and the
NTF has not proven highly accurate when compared with clinical
data. Alternative methods of preclinically testing ophthalmic
lenses, such as wavefront aberration techniques, have similarly not
comported well with clinical data. Further, MTF, wavefront
aberration, and other modeling tests have been generally applied to
only specific vision conditions, and hence offer only limited
predictability of VA and other vision factors, such as contrast
sensitivity.
[0009] Forty-six (46) physiological eye models for a population of
eyes of particular conditions and having particular characteristics
have recently been produced by Piers, et al. (See P. A. Piers, H.
A. Weeber, P. Artal, and S. Norrby, "Theoretical comparison of
aberration correcting customized and aspheric intraocular lenses,"
J Refract Surg 23(4), 374-384 (2007), incorporated herein by
reference as if set forth in the entirety). The Piers models
provide clinically-based models of the eye, and encompass a
representative model of the clinical population. The representative
models include the aberrations that may be presented in the eye,
which may additionally indicate the characteristics of the eye,
such as the optical length of the eye, the corneal curvature, the
pupil size, and variations thereof, for example. More specifically,
the Piers eye models demonstrate wavefront aberrations that are
characteristic for a cataract population, and which have been
verified against clinical data for contrast vision.
[0010] Although modeling techniques are available, as discussed
above, and although the Piers models provide simulated eyes to
which modeling techniques may be applied, modeling techniques have
not been applied to model eyes in a manner that allows for the
evaluation and optimization of clinical implementations of lenses
designed using the modeling. More particularly, the available art
fails to provide feedback from clinical implementations to evaluate
and optimize lens design, and to thereby improve the obtained
characteristics of subsequent clinical implementations.
[0011] Thus, the need exists for an apparatus, system and method
for predictive modeling to design, evaluate and optimize ophthalmic
lenses.
SUMMARY OF THE INVENTION
[0012] The present invention is and includes an apparatus, system
and method for predictive modeling to design, evaluate and optimize
ophthalmic lenses. Ophthalmic lenses may include, for example,
contacts, glasses or intraocular lenses (IOLs).
[0013] A system for designing, optimizing and evaluating a clinical
implementation of an ophthalmic lens may include a plurality of eye
models associated with at least one processor, wherein ones of the
eye models are indicative of at least one condition of an eye, and
a simulator instantiated by the at least one processor that models
the ophthalmic lens in at least one of the eye models. The
simulator may output at least one simulated outcome, including at
least first characteristics of the eye models into which is placed
the ophthalmic lens. The system may further include at least one
clinical input to the at least one processor, wherein the clinical
input includes at least an outcome indicative of the clinical
implementation of the ophthalmic lens, and a comparator
instantiated by the at least one processor that compares at least
the simulated outcome and the clinical outcome, and that compares
the differences between the simulated outcome and the clinical
outcome to a predetermined tolerance threshold. The at least one
processor may optimize the one of the eye models and/or the
simulator in order to bring the differences within the
predetermined tolerance threshold.
[0014] In accordance with the present invention, the outcomes in
the exemplary system, method and apparatus may include, for
example, performance criteria of the eye, such as visual acuity and
contrast sensitivity, for example. The predetermined tolerance
threshold may preferably relate to these performance criteria of
the eye, for example. Similarly, the conditions may include viewing
conditions, such as object distance, light level, off-axis vision,
object contrast, visual task, and the like. The characteristics of
the eye may include pupil size, off-axis vision, corneal optical
power, optical aberrations, such as chromatic and spherical
aberration, astigmatism, coma, trefoil, and residual accommodation,
for example. Characteristics may also be or be derived from patient
group characterizations, such as post-Lasik patients, myopes,
patients with neural or retinal conditions, any of which
characterizations may be may be delineated by gender, age and race,
for example.
[0015] An exemplary system may further include, for example, the
clinical input from a local input, and/or the clinical input from a
remote input, such as a remote input conveyed across at least one
network. Correspondingly, the network aspects of the present
invention may allow for provision of a computerized graphical user
interface embodying the aspects of the present invention.
[0016] A method of optimizing, evaluating or designing an
ophthalmic lens may include receiving a first lens design for the
ophthalmic lens, simulating, for example, the performance provided
by the first lens design in at least one modeled eye having a
plurality of first characteristics, and receiving clinical
performance of the first lens design in at least one patient eye
having the plurality of first characteristics. The exemplary method
may further include comparing the clinical performance and the
simulated performance, and optimizing the first lens design to a
second lens design in accordance with the comparing. The optimizing
may include, for example, at least modifying the simulating
step.
[0017] The first and second lens designs may have associated
therewith, for example, a parameter, such as a visual acuity and/or
a contrast sensitivity, that may provide the basis for the
comparing and optimizing steps. The characteristics may include
performance characteristics, and/or conditions, topologies, or
aspects of the eye, for example. Further, the comparing step and/or
the optimizing step of the exemplary method may include weighting
of, for example, ones of the characteristics, and/or of the
simulated or clinical performance.
[0018] An exemplary apparatus in accordance with the present
invention may include a design tool for designing a lens for
implantation in an eye having a plurality of characteristics, a
simulator for simulating performance of the lens in at least one
modeled eye having the plurality of characteristics, at least one
input for receiving clinical performance of the lens in the eye
having the plurality of characteristics, a comparator for comparing
outcomes of the clinical performance and the simulated performance,
and an optimizer for optimizing a subsequent one of the outcomes of
the clinical performance responsive to modification of the lens in
accordance with at least one modification to the simulated
performance.
[0019] Thus, the present invention provides an apparatus, system
and method for predictive modeling to design, evaluate and optimize
ophthalmic lenses.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Understanding of the disclosure will be facilitated by
consideration of the following detailed description of the
embodiments, taken in conjunction with the accompanying drawings,
in which like numerals refer to like parts and in which:
[0021] FIG. 1 is an illustration of an eye in the natural
state;
[0022] FIG. 2 is an illustration of an eye having an intraocular
lens;
[0023] FIGS. 3a, 3b and 3c are flow diagrams illustrating a method
for optimizing an ophthalmic lens in accordance with the present
invention;
[0024] FIG. 4 is a block diagram illustrating a system for
optimizing an ophthalmic lens in accordance with the present
invention;
[0025] FIGS. 5a-5d are histograms illustrating eye characteristic
distributions for model eye populations;
[0026] FIG. 6 is a plot illustrating sVA plots across 46 Piers eye
models;
[0027] FIGS. 7a-7c are differential plots illustrating clinical VA
versus sVA over a range of defocus for different eye
characteristics;
[0028] FIG. 8 is a correlation plot for clinical VA versus sVA;
[0029] FIG. 9 is a plot of VA versus defocus for a simulated
population and a clinical population;
[0030] FIG. 10 is a correlation plot for clinical VA versus
sVA;
[0031] FIG. 11 is an illustration of an outcome for clinical VA
versus sVA;
[0032] FIG. 12 is an illustration of a desired defocus curve;
and
[0033] FIG. 13 is an illustration of an outcome for clinical VA
versus sVA.
DETAILED DESCRIPTION OF THE INVENTION
[0034] It is to be understood that the figures and descriptions of
the present invention have been simplified to illustrate elements
that are relevant for a clear understanding of the present
invention, while eliminating, for the purposes of clarity, many
other elements found in typical optical and optical simulation
apparatuses, systems and methods. Those of ordinary skill in the
art will recognize that other elements are desirable and/or
required in order to implement the present invention. However,
because such elements are well known in the art, and because they
do not facilitate a better understanding of the present invention,
a discussion of such elements is not provided herein.
[0035] The apparatus, system and method of the present invention
may be predictive as to the performance of ophthalmic lenses, such
as IOLs, in the eye under any of a variety of circumstances, and
with respect to any of a variety of ocular conditions and eye
types, and may provide for improved performance of ophthalmic
lenses optimized using the modified models indicated by the
correspondent clinical results. For example, the present invention
may include application of mathematical modeling of certain
characterizations of the eye, such as visual acuity (VA) and/or
contrast sensitivity, to a series of physiological eye models, such
as the 46 physiological eye models provided by Piers, et al., and
comparison of model output to actual clinical data, wherein
feedback from the actual clinical data may be employed to evaluate
and/or modify one or both of the applied mathematical model and the
physiological eye model to better approximate one or more
indications of the clinical results.
[0036] For example, clinical results may be employed in the present
invention to determine ones or groups of the 46 model eyes of
Piers, such as 1, 10, or all eye models, that optimally model an
eye having particular characteristics. Thus, the present invention
may provide more accurate model eyes correspondent to clinical eyes
having particular characteristics.
[0037] It will be appreciated by those of ordinary skill in the
pertinent arts that the apparatus, system and method of the present
invention may be embodied in one or more computing processors,
associated with one or more computing memories, within which is
resident computing code to execute the mathematical models
discussed herein, to provide the physiological models discussed
herein, to track, such as in a relational database, the unique
clinical characteristics of the clinical eyes discussed herein,
preferably in conjunction with the applied physiological model, and
to accumulate feedback regarding the clinical outcome of ophthalmic
lenses designed using the system, apparatus and method of the
present invention.
[0038] Further, those skilled in the art will appreciate, in light
of the disclosure herein, that the aspects of the present
invention, and most particularly the feedback of the present
invention, may be provided to the one or more computing processors
for processing via one or more computing networks, including via
one or more nodes of a computing network. Computing networks for
use in the present invention may include the Internet, an intranet,
an extranet, a cellular network, a satellite network, a fiber optic
network, or the like. More particularly, the networking aspects of
the present invention may allow for provision of the modeling
techniques discussed herein to the offices of a myriad of
ophthalmic practitioners of one or more selected types, throughout
a selected region, throughout a country, or throughout the world,
and additionally may allow for the monitoring of clinical test
results and the feeding back of those test results from the offices
of those practitioners to the apparatus, system and method of the
present invention.
[0039] FIG. 1 is an illustration of an eye in the natural state.
Eye 10 includes retina 12 for receiving an image, produced by
cornea 14 and natural lens 16, from light incident upon eye 10.
Natural lens 16 is disposed within capsular bag 20, which separates
anterior and posterior chambers of eye 10. Iris 26 may operate to
change the aperture, i.e. pupil, size of eye 10. More specifically,
the diameter of the incoming light beam is controlled by iris 26,
which forms the aperture stop of eye 10.
[0040] Capsular bag 20 is a resilient material that changes the
shape and/or location of natural lens 16 in response to ocular
forces produced when ciliary muscles 22 contract and stretch
natural lens 16 via zonules 24 disposed about an equatorial region
of capsular bag 20. This shape change may flatten natural lens 16,
thereby producing a relatively low optical power for providing
distant vision in an emmetropic eye. To produce intermediate and/or
near vision, ciliary muscles 22 contract, thereby relieving tension
on zonules 24. The resiliency of capsular bag 20 thus provides an
ocular force to reshape natural lens 16 to modify curvature to
provide an optical power suitable for required vision. This change,
or "accommodation," is achieved by changing the shape of the
crystalline lens. Accommodation, as used herein, includes the
making of a change in the focus of the eye for different
distances.
[0041] FIG. 2, there is shown an eye having natural lens 16
replaced with an IOL 100. Natural lens 16 may require removal due
to a refractive lens exchange, or due to a disease such as
cataracts, for example. Once removed, natural lens 16 may be
replaced by IOL 100 to provide improved vision in eye 10. Eye 10
may include IOL 100 with optic 102, cornea 14, retina 12, and
haptics or support structure 104 for centering optic 102. The
haptics 104 may center optic 102, and may transfer ocular forces
from ciliary muscle 22, zonules 24, and/or capsular bag 20 to optic
102 to change the shape, power, and/or axial location of optic 102
relative to retina 12.
[0042] FIG. 3a is a flow diagram illustrating a method 300 of
optimizing an ophthalmic lens 100, such as, for example, the IOL
illustrated in FIG. 2, in accordance with the present invention. In
the illustrated method 300, an ophthalmic lens may be designed
and/or provided at step 304 for modeling and for clinical
application. Such a lens may, for example, have associated
therewith a particular design parameter or parameters. The design
parameter or parameters may comprise an optical shape determined by
inputting a set of patient parameters into an optimizer. The design
parameter or parameters may be derived by the optimizer by
determining at least one coefficient of a set of Zernike
polynomials. For example, calculating the design parameter or
parameters often includes determining a plurality of selected
Zernike coefficients, such as for power, astigmatism, or chromatic
or spherical aberration, for example, at various orders. The
outcome of the eye at a first viewing condition may be measured
while viewing at a first viewing distance, and the outcome of the
eye at a second viewing condition may be measured while viewing at
a second viewing distance which is less than the first
distance.
[0043] At step 306, the aforementioned eye models, such as a subset
of the aforementioned eye models having topologies and/or
characteristics of interest, may be subjected to simulated visual
acuity (sVA) modeling at step 308 to predict, for example, VA
and/or other factors, such as contrast sensitivity. In the
aforementioned example, model eyes similar in characteristics to
the eye for which the design parameter or parameters are to be
obtained may be subjected to step 308. Characteristics of interest
may include any one or more of a plurality of patient parameter
inputs, such as pupil sizes, off-axis vision, and object distance
vision, corneal optical power, or residual accommodation by gender,
age or race, for example. Similarly, characteristics may include
particular aberrations, and/or aberrations of particular severity,
such as astigmatism, presbyopia, and chromatic and spherical
aberration, for example.
[0044] At step 310, weighted factors to optimize vision and/or
performance of lens 100 may be applied, wherein the factors may
include, for example, clinical performance of the lens 100 as
designed at step 304. Weight factors may be assigned, for example,
to each of the characteristics and/or topologies discussed above.
For example, the parameters of an ophthalmic lens may be
determined, at least in part, by iteratively optimizing a threshold
tolerance, such as by optimizing a patient's VA obtained with the
corrective parameters, wherein the patients have a clinical eye
characterized similarly to the characteristic eye model or models
selected.
[0045] The lens 100 may be evaluated, adjusted and/or optimized at
step 312, such as a clinical adjustment to the designed lens for
actual implantation, or such as a modification to the model of the
designed lens for feedback to step 306. For example, the parameters
may be optimized for a characteristic eye by scaling a refractive
shape, and/or by analytically or numerically deriving an optical
shape providing the desired optical powers at an associated
plurality of viewing conditions. The modification to the lens
design for modeling may be made pursuant to failure of the lens
design to meet a predetermined threshold tolerance in clinical
implementation, and may include verification of the current lens
design or recommendation of an alternative lens altogether.
[0046] FIG. 3b is a more detailed illustration of the method 300 of
FIG. 3a. As illustrated in FIG. 3b, an IOL design may be provided,
based on, for example, a design estimation or a design software
simulation for particular input conditions, at step 304. At step
306, ones of a plurality of available eye models, preferably in
accordance with the particular input conditions used in step 304,
are selected. A simulation may be provided, into which are
incorporated the lens design from step 304 and the eye models from
step 306, at step 308a, which may generate a simulation outcome at
step 308b. A patient group for obtaining clinical outcomes may be
obtained at step 320, into which are incorporated the lens design
from step 304, and to which patient group clinical testing may be
applied at step 322. The clinical testing provides clinical
outcomes at step 324. Optimization of the lens design at step 304
or the simulation at step 306 may be provided at the optimization
step 312, which step 312 may be performed in accordance with a
weighting of input conditions and/or simulation outcomes at step
310 (shown in FIG. 3a). For example, at step 310 pupil size may be
weighted 2 to 1 over spherical aberration, or VA may be weighted 2
to 1 over contrast sensitivity, in obtaining a desired optimization
at step 312.
[0047] Further, and as illustrated by way of example with respect
to FIG. 3c, the method 300 of FIGS. 3a and 3b may be iteratively
provided in order to arrive at an improved lens design at step 330.
More specifically, a lens may be provided at step 304, and an
optimization may be iteratively provided at step 312, with or
without the clinical outcome input at step 324, to generate an
improved lens design at step 330. Of course, the iteration of step
312 may be performed without clinical outcome input in the event of
pre-clinical design, but may preferably include clinical outcome
input in order to optimize the simulation for improved clinical
lens design output at step 330. Further, the iteration of step 312
may lead to a new or alternative lens design, a modification to an
original lens design, or a modification to simulation at step 306,
for example.
[0048] The threshold tolerance of the present invention is
predetermined, and may be predetermined based on literature in the
art, based on experience of a patient or group of patients, or from
a back-calculation from obtained clinical data, for example. For
example, the threshold function may reflect optical quality
throughout a particular range, and/or may represent patient
tolerable parameters. The threshold function may comprise a ratio
of an optical parameter of the eye with a diffraction theory
parameter. Thus, exemplary tolerance thresholds may include a
threshold of at least one parameter selected from the Strehl Ratio
(SR), the MTF, the VA, the point spread function (PSF), the
encircled energy (EE), the MTF volume or volume under MTF surface
(MTFV), the compound modulation transfer function (CMTF), the
contrast sensitivity (CS), and/or dysphotopsia, at any or all
predefined viewing conditions. Similarly, the threshold tolerance
may also be based on geometrical optics and/or ray tracing.
Preferably, minimization or maximization of the threshold tolerance
should yield a predictable optimized optical quality of the eye.
For example, the threshold tolerance may be a function with a
certain number of free parameters to be optimized, via minimization
or maximization, through the optimization feedback discussed
herein.
[0049] More specifically, the threshold tolerance for optimization
may be a function of the MTF. As referenced above, MTF may be used
to predict visual performance, in part because the MTF at one
spatial frequency corresponds to one angular extent of targets. The
modulation transfer function (MTF) may be calculated using the
formula:
MTF(u,v)=FT[PSF(x,y)] MTF(u,v)=Re[GPF(x,y)GPF(x,y)]
where u and v represent spatial frequencies, Re represents the real
part of a complex number, FT represents a Fourier Transform, GPF
represents a generalized pupil function, and x and y represent
position or field of view.
[0050] For example, in a particular exemplary embodiment, the
visual acuity in the presence of defocus of a population of
cataract patients implanted with diffractive multifocal intraocular
lenses may be optimized using method 300. At step 306, method 300
may use the aforementioned set of cataract patient physiological
eye models, and/or may use a subset of the physiological eye models
having particular eye characteristics and/or topologies, such as a
subset of eye models or single eye models that include particular
chromatic and higher order aberrations, for example.
[0051] The set or subset of the eye models may then be subjected to
a simulation, such as a simulated VA modeling, at step 308, and the
results compared with clinical outcomes at step 310. Optimization
based on the clinical outcome comparisons may be obtained using
standard optimization routines, such as the downhill simplex method
or the direction set method. The downhill simplex method, for
example, starts with an initialization of N+1 points or vertices to
construct a simplex for an N-dimensional search, and iteratively
endeavors to reflect, stretch, or shrink the simplex by geometrical
transformation so that a close-to-global minimum or pre-defined
accuracy is found. A more detailed discussion of optimization in an
optical system using the downhill simplex and direction set method
may be found in U.S. Pat. No. 7,475,986, entitled "Presbyopia
Correction Using Patient Data", issued Jan. 19, 2010 and having
inventors Dai, et. al, the entire disclosure of which is
incorporated herein by reference as if set forth in the
entirety.
[0052] Simulated VA (sVA) may be determined by first calculating
the MTF for each eye model at varying levels of defocus. The sVA is
then calculated from the spatial frequency at which the MTF
intersects with the neural threshold function (NTF). In this
exemplary embodiment, the sVA produced for the eye models may be,
for example, systematically 0.05 log MAR units lower (better
acuity) than the clinical results, although the difference from the
clinical results may be independent of defocus (p=0.98). If either
of the simulated versus clinical or the dependence on defocus
measures is outside of tolerance threshold, i.e., if the sVA in the
eye models differed grossly from the clinical results outside of
the predetermined tolerance, and/or if the difference between the
modeled and clinical results was highly dependent on defocus, the
method 300 may feedback, at step 312, a weighting of the
characteristics not within tolerance to indicate an optimization to
one or both of the sVA model or the subset of eye models used at
step 306.
[0053] More particularly, for example, the weighting factors may,
as discussed above, be made with respect to the characteristics
and/or topologies employed in the selected eye models, and/or may
additionally be with respect to the desired clinical outcome. As
such, an improved VA may be weighted at 2 to 1 as compared to
contrast sensitivity, while a VA may additionally be defined, with
respect to field angles, as weighting far vision over near, forward
angle over peripheral, and the like, for example.
[0054] Method 300 may be similarly employed for more complex
lenses, such as for a population of cataract patients implanted
with multifocal intraocular lenses (IOLs). Multifocal IOLs may
supply two simultaneous focal points, one for distant objects and
one for near objects, and may additionally provide a depth of focus
that results in improved visual performance at intermediate
distances. Such a multifocal IOL is the diffractive Tecnis.RTM.
Multifocal IOL (model ZM900) by Abbott Medical Optics Inc. Those
skilled in the pertinent arts will appreciate, in light of the
discussion herein, that other multifocal lenses may be used in the
instant invention.
[0055] For example, the Tecnis Multifocal IOL design has a
diffractive relief pattern on the posterior surface, and splits
light in equal amounts to the far and near focus. The diffractive
add power for near vision, for example, is +4 diopters. The Tecnis
lens additionally provides an aspheric surface on the anterior side
of the optic to reduce spherical aberration.
[0056] Cataract patients having certain eye characterizations may
be bilaterally implanted with the Tecnis lens. For example, only
patients with natural pupil diameters between 2.5 and 4.0 mm under
photopic lighting conditions may be implanted. Such an eye
characteristic may be used to select and/or weight ones of the
available models eyes, and or may be weighted against other eye
characteristics in the model eyes used, for example.
[0057] Several methods may be used to measure the pupil diameter
using a pupilometer, such as image analysis techniques and
wavefront measurements, including Wavescan RTM (VISX, Incorporated,
Santa Clara, Calif.) wavefront measurements, for example. The size
of the pupil may at least partially determine the amount of light
that enters the eye, and may also have an effect on the quality of
the image formed by the light entering the eye. For example, when
the pupil is very constricted, a relatively small percentage of the
total light falling on the cornea may actually be allowed into the
eye. In contrast, when the pupil is more dilated, the light allowed
into the eye may correspond to a greater area of the cornea. Of
note, accommodation and pupillary constriction work in unison in
the healthy eyes when shifting from a far to a near viewing
distance, and a fairly linear relation may thus exist between at
least a portion of the pupillary constriction and accommodation
ranges.
[0058] A standardized defocus test may be performed on each
implanted patient, such as using a self-calibrating,
self-illuminating (85 cd/m.sup.2) EDTRS chart placed at 4 meters in
front of the patient. Defocus may be introduced by placing
successive minus trial lenses in 0.5 D increments over the
patient's best distance correction. The relationship between
defocus and viewing distance may be determined by: [viewing
distance expressed in meters]=1/[defocus expressed in diopters].
Binocular visual acuity at each defocus position may then be
measured, and may be from zero (best correction) to -5 diopters at
each defocus position, for example. The measured VA at defocus may
be used to select or limit the selected eye models in the
simulation, and/or may be used to enhance the modeling.
[0059] For each individual eye model, the power of the multifocal
IOL may be determined, such as based on the eye length and corneal
power. A lens may be modeled at a range of 12-25 mm, and preferably
20 mm, in front of the cornea of the modeled eye, and the imaging
of the eye models may be traced and refracted for both spherical
and cylindrical power. Defocus testing may then be simulated by:
placing a point source target at 4 meters in front of the anterior
cornea, i.e., at a distance correspondent to that used in the
clinical setting; fixing the eye model to a fixed physical pupil
diameter of 3.0 mm, corresponding to an average apparent pupil size
of approximately 3.3 mm in the clinical setting; varying the power
of the spectacle lens in 0.25 diopter increments to obtain defocus;
and, at each defocus position, calculating the sVA.
[0060] Pursuant to the sVA modeling, differences between the
clinical outcomes, on average, and the aforementioned eye models
may indicate a need for modification of one or both of the sVA
model or the eye models used. Needless to say, the larger the
sample size that is fed back through the system to the sVA model
and the eye models, the more likely any correction to the modeling
will be properly indicative of clinical optimization. As such, the
present invention is advantageous in that it may provide a
networking among large numbers of practitioners, and thus may
accept a large volume of feedback. Further, the feedback provided
may be corresponded to the sVA modeling of the eye models used,
thus enabling a correspondence, over a large sample size, between
sVA modeling of the eye models and clinical outcomes.
[0061] FIG. 4 is a block diagram illustrating system 400 in
accordance with the present invention. The illustrated system 400
may include at least one processor 402, having associated therewith
a plurality of eye models 404, such as the Piers models or subsets
thereof, the Liou and Brennan model, the Seidel aberration model,
or the Le Grandoye, for patients having particular aberrations,
characterizations and/or topologies, and simulator 406. Simulator
406 may be any type of modeling software capable of modeling an
ophthalmic lens 100 of a given design in at least one of the eye
models 404 provided. Simulator 406 may be embodied as Code V, OSLO,
Zemax, ASAP, and similar software modeling programs, for example.
The at least one processor 402 applies simulator 406 to at least
one eye model 404 to output a simulation 410 of eye
characteristics, such as VA and/or contrast sensitivity.
[0062] System 400 further includes at least one remote, and may
include at least one local, clinical input 412. Clinical input 412
may be provided over at least one network 414, and is input to at
least one processor 402 for comparison to the characteristics of
simulation output 410. In the event that simulation output 410 is
deficient, such as showing suboptimal variance from clinical input
412, processor 402 may act as an optimizer 420 to modify at least
one of selected eye models 404, the weights associated with the
selected eye models 404 and/or the characteristics of the eye, and
the actions of simulator 406, in order to obtain a more suitable
simulation output 410 in light of clinical input 412.
[0063] As will be understood by those skilled in the pertinent
arts, clinical input 412 may, in certain exemplary embodiments, be
an average over a predetermined number, or over all, clinical
outcomes having one or more predetermined, or all, common
topologies and/or characteristics of the patient eye.
Correspondingly, eye models 404 used may be a simulated or
clinically obtained average over a population having particular
characteristics, a simulated or clinically obtained average over a
population generally, a simulation matched to a particular patient,
a patient type (i.e., cataract patient or LASIK patient), a group
of patients, or the like.
[0064] More particularly with respect to one of the exemplary
embodiments discussed above with respect to FIGS. 3, optimizer 420
may use clinical feedback to design an ophthalmic lens that
optimizes patient parameters for patients having certain eye
characteristics. For example, the optimizer 420 may optimize the
parameters of lenses designed at simulator 406, wherein the
simulator initially designed the lens based on patients of a
particular pupil diameter and/or having a desired optical power,
and wherein optimizer 420 determines variations to the simulation
410 at simulator 406 based on failure of clinical results to meet
the desired optical power tolerance threshold for that parameter(s)
in patients having the designed-for pupil diameter.
[0065] Moreover, and as referenced above, the method of FIGS. 3 and
the system of FIG. 4, and the exemplary embodiments set forth
herein, may be implemented using computer hardware and software on
a computer readable medium, and the computing hardware and software
may preferably be connected to at least one computing network. The
software may, for example, be implemented by system 400 to perform
method 300, such as via a graphical user interface (GUI) provided
in a clinical setting. The GUI may provide any number of graphical
panels for convenient use by a clinician, such as three primary
panels. The primary panels may include, for example, an optical
parameter panel, a clinical outcome input 412 panel, and a
recommended action panel that may be based on simulation 410.
Needless to say, one or more of the graphical panels may be divided
into sub-panels, such as sub-panels for optimizations,
verifications, graphs, images, historical data, and the like. The
software GUI may also provide menu bars, tool bars, status bars,
drop down menus, and the like.
[0066] Method 300 and system 400 may be applied to a given
population of modeled eyes having predetermined characteristics
and/or topologies, such as in order to gain a sufficient sample
size to enable optimization of the lens design, the selected eye
models, and/or the simulation methodology based on clinical
findings. FIGS. 5a-5d are histograms illustrating eye
characteristics for a population of model eyes. As illustrated,
FIG. 5a shows the corneal power of the eye across a population of
model eyes. FIG. 5b shows the description of the topography of a
population of model eyes by a 4.sup.th order Zernike polynomial. As
will be understood to those skilled in the art, Zernike polynomials
describe aberrations of the lens from an ideal spherical shape,
which aberrations may result in refraction errors.
[0067] The histogram of FIG. 5c illustrates the axial length of the
eye across a population of model eyes. Finally, FIG. 5d is a
histogram of the magnitude of the total refractive errors of the
eye across a population of model eyes using root-mean squares
(RMS). For example, a minimum root-mean-squares (RMS) error may be
used to determine the accommodation during different conditions.
For instance, if no aberrations are present, and there is 2 D of
residual accommodation, such a patient uses 0.5 D of residual
accommodation when visualizing a target at 2 meters. Moreover, the
patient uses all 2 D of residual accommodation to view a target at
0.5 meters. However, the patient would have difficulty viewing
targets closer than 0.5 meters, as the residual accommodation is
exhausted and no longer available.
[0068] While FIGS. 5 illustrates characteristics of populations of
eyes within the Piers physiological models, FIG. 6 is a plot
illustrating the sVA across the 46 Piers eye models. As shown, the
sVA is plotted against defocus, wherein the defocus is varied to
simulate performance of the eye. Upon simulation, ones of the eye
models having similar characteristics and/or topologies to the
clinical eye(s) of interest may be plotted against the clinical
eye(s) of interest, such as to obtain a comparative plot of the sVA
and clinical performance. Such comparative plots are illustrated in
FIGS. 7, and may be used in the method 300, for example, in order
to assess optimizing modifications to be made to at step 312 of
FIGS. 3.
[0069] FIGS. 7a-7c are plots illustrating sVA (light plot) and
clinical VA (dark plot) versus defocus, for a population of eyes
having varying pupil characteristics. Defocus has is mathematically
expressed above. For example, FIG. 7a illustrates a comparison of
average clinical VA and sVA for a set of eyes having a small pupil,
FIG. 7b illustrates the same comparison for a set of eyes having a
medium pupil, and FIG. 7c illustrates the comparison for a set of
eyes having a large pupil.
[0070] As indicated by the plots of FIGS. 7, clinical VA and sVA
for a set of modeled eyes may vary over a range of defocus for
different eye characteristics. This variation over the range of
defocus may also provide secondary characteristics via the
comparison, such as the plot of FIG. 8 showing the correlation
between clinical VA and sVA. As discussed above, particularly with
respect to FIGS. 3 and 4, the difference between the clinical VA
and sVA, and/or the secondary characteristics of the difference,
may have an accepted tolerance threshold within which the
simulation is deemed to acceptably define the clinical
implementation of the lens. More particularly, a designed lens may
be modified until the lens is optimized in accordance with the
simulation, and that lens, once clinically implemented, may be
deemed clinically optimal if the clinical VA is within the
predetermined tolerance of the sVA. Conversely, if the clinical VA
is not within tolerance of the sVA, the difference in the plots of
FIGS. 7, and/or the secondary characteristics of the difference as
shown in FIG. 8, may be used in the method of FIGS. 3 and the
system of FIG. 4 as feedback to modify the simulation. Modification
of the simulation may include, for example and as discussed
hereinabove, variation in the simulation and/or calculations,
changes in the weighting factors, or the weighting of the factors,
to modify the lens design, and/or changes to the selected model
eye(s).
[0071] By way of specific example, the optimization method 300 and
system 400 may be applied to a particular IOL design, such as
design model 911A by Abbott Medical Optics Inc., and may
subsequently dictate the use of an alternative design, such as a
multifocal IOL. In this illustration, the model 911A is clinically
tested in three groups of normal cataract patients, having small,
medium, and large pupils. The clinical outcome of VA as a function
of defocus is illustrated in FIG. 9, wherein each line shown
depicts an average outcome for the respective group of
patients.
[0072] For the sVA, three sets of the 46 Piers eye models,
representing a normal cataract population and having essentially
the same or similar characteristics (small, medium and large pupil
sizes) to the eyes in the clinical groups, are selected. The design
of model 911A is modeled for the sVA as a function of defocus, and
the results are illustrated in FIG. 9, wherein each line depicts
the average outcome for the respective set of eye models.
[0073] The differences between the simulation outcome, namely the
sVA in this example, and the clinical outcome, namely the VA in
this example, are assessed. FIG. 10 illustrates the correlation
between the sVA and the clinical VA, wherefrom the differences
between the sVA and the clinical VA may be assessed.
[0074] FIG. 11 illustrates the optimization of the modeled lens
based on the assessed differences, and compares the optimized
simulated outcome to the clinical outcome. The optimized simulation
may be used to generate a new IOL design to be optimally provided
for the same groups of cataract patients. In this exemplary
embodiment, a multifocal IOL design may be employed to provide the
desired visual acuity shown in FIG. 12.
[0075] An IOL design that matches the desired outcome, according to
the simulation outcome, is the aforementioned Tecnis Multifocal IOL
model ZM900. The IOL model ZM900 may thus be implanted in the three
groups of normal cataract patients having small, medium, and large
pupils. The clinical outcome, expressed as VA as a function of
defocus, is illustrated in FIG. 13, wherein each line depicts the
average outcome for each group of patients. The differences between
the sVA and the clinical VA may again be assessed, and used to
iteratively optimize the simulator, or the recommended lens, as
desired, for example. In accordance with FIG. 13, those skilled in
the art will appreciate that the use of simulation in the present
invention resulted in a lens design, namely model ZM900 in this
example, that closely matched clinical performance, although the
simulation was based on a different lens design, namely model 911
in this example.
[0076] Needless to say the illustration immediately hereinabove is
provided by way of example only, and may be applicable to lens
design, modification of physical lens design, modification to
simulation, modification to selections of eye models, and the like.
Similarly, the illustration is applicable to not only groups of
patients, or with regard to current lens designs, but is equally
applicable to custom and quasi-custom lens design, for individual
patients and limited or unique subsets of patients,
respectively.
[0077] Although the invention has been described and pictured in an
exemplary form with a certain degree of particularity, it is
understood that the present disclosure of the exemplary form has
been made by way of example, and that numerous changes in the
details of construction and combination and arrangement of parts
and steps may be made without departing from the spirit and scope
of the invention.
* * * * *